Discrete Experimental Data Research Articles

Comparison of radiant intensity in aqueous media using experimental and numerical simulation techniques

Accurately modelling the propagation of radiant intensity in aqueous environments poses significant challenges for both academia and industry, due to complex interactions like absorption, scattering, and reflection. This study aims to improve the accuracy of optical modeling in water-based systems by comparing experimental data with numerical simulation techniques, addressing the need for more reliable simulation methods in multiple applications like treatment of water and environmental monitoring.Implementation has been done by analyzing how the method compares with the discrete ordinate method, radiometry, and actinometry. The study further quantifies the effect of the photoreactor quartz tube on measured intensity for multiple wavelengths. Losses in light intensity are estimated to be 10 ± 0.5% for FX-1 265 source. In contrast, the simulation in a water medium showed an increase of up to 64% in the light intensity delivered to the central part of the tube due to internal reflections and scattering. Model predictions from ray tracing successfully compared with the Discrete Ordinate Method (DOM) and experimental data (within ± 6%), ensuring the accurate design of complex systems for water disinfection. The data from simulations is seen to tackle challenges faced in complex radiation modeling and demonstrates that the method can be utilized as a useful tool for optimization and prediction.

Determination of flexural stiffness of composite timber beams using DIC and wavelet transform of deflection lines

The article presents an experiment of four-point bending of timber I-beams, the deflections of which were measured using digital image correlation (DIC). The deflection functions were subjected to a wavelet transform operation, which used the second derivative of the Gaussian distribution to determine the curvature of the beams’ axis. On this basis, the distribution of flexural stiffness of the elements was estimated, showing the potential usefulness of the DIC technique in production control and laboratory testing of prefabricated timber elements. The limitations of the proposed research method related to the need to perform numerical integration operations on discrete experimental data subject to measurement errors are also presented

Comparison of radiant intensity in aqueous media using experimental and numerical simulation techniques.

Accurately modelling the propagation of radiant intensity in aqueous environments poses significant challenges for both academia and industry, due to complex interactions like absorption, scattering, and reflection. This study aims to improve the accuracy of optical modeling in water-based systems by comparing experimental data with numerical simulation techniques, addressing the need for more reliable simulation methods in multiple applications like treatment of water and environmental monitoring.Implementation has been done by analyzing how the method compares with the discrete ordinate method, radiometry, and actinometry. The study further quantifies the effect of the photoreactor quartz tube on measured intensity for multiple wavelengths. Losses in light intensity are estimated to be 10 ± 0.5% for FX-1 265 source. In contrast, the simulation in a water medium showed an increase of up to 64% in the light intensity delivered to the central part of the tube due to internal reflections and scattering. Model predictions from ray tracing successfully compared with the Discrete Ordinate Method (DOM) and experimental data (within ± 6%), ensuring the accurate design of complex systems for water disinfection. The data from simulations is seen to tackle challenges faced in complex radiation modeling and demonstrates that the method can be utilized as a useful tool for optimization and prediction.

Optimal Design of Validation Experiment for Material Deterioration.

For the deterioration model of a material, it is crucial to design a validation experiment to determine the ability of the deterioration model to simulate the actual deterioration process. In this paper, a design method of a validation experiment for a deterioration model is proposed to obtain the experiment scheme with low cost and satisfactory credibility. First, a normalized area metric based on probability density functions for the deterioration model is developed for validation results quantification. Normalized area metrics of different state variables in an engineering system can be applied to a unified evaluation standard. In particular, kernel density estimation is used to obtain smooth probability density functions from discrete experimental data, which can reduce the systematic error of the validation metric. Furthermore, a design method for the validation experiment for the deterioration model is proposed, in which the number of experimental samples and observation moments in each experimental sample are design variables, while the credibility of the validation experiment is the constraint. For the experiment design, the problem with varying dimensions of design variables occurred in the optimal design. Thus, a collaborative optimization method using the Latin hypercube sampling was developed to solve this problem. Finally, the results of the two examples showed the characteristics of the proposed metric and also reflected the correlation between the design variables and experimental credibility.

Revisiting preferences for agricultural insurance policies: Insights from cashew crop insurance development in Ghana

The development and uptake of agricultural insurance products by farmers in developing countries has been universally and disappointingly low. This paper investigates farmers’ preferences and willingness to pay for a variety of agricultural insurance products, including indemnity insurance, index insurance, benchmark insurance, and hybrid (indemnity-index) insurance in the Bono and Bono East Regions of Ghana. We employed hybrid latent class and multiple indicators, multiple causes (MIMIC) models using discrete choice experimental data from 383 cashew growers. The results show that cashew farmers are heterogeneous in their preferences, with a majority advocating for agricultural insurance against key perils such as wildfires, high wind speed and excess rainfall. Hybrid (indemnity-index) insurance product is highly preferred and valued by cashew farmers advocating for agricultural insurance, followed by index insurance product. Farmers are quite sensitive to premiums, expected payout, type of perils covered by the insurance and loss assessment criteria. Social and behavioural constructs relating to trust in insurance companies, subjective knowledge about agricultural insurance, and perceived agricultural insurance benefits are significant determinants of farmers’ preferences for agricultural insurance products. The findings imply that it has become very necessary for agricultural insurance product developers, underwriters, and insurers in developing countries to gain more insight on farmers’ social and behavioural constructs related to agricultural risk, insurance knowledge and trust. We suggest that agricultural insurance product developers and policy-makers involved in agricultural insurance development should improve farmers’ understanding of basis risk and the concept of agricultural insurance, as well as the potential benefits of farm insurance. In this way, we can improve the uptake of agricultural insurance products by farmers in developing countries.

The legitimacy of decoupled dynamic flow stress equations and their representation based on discrete experimental data

In this study, the legitimacy of a decoupled empirical dynamic flow stress equations, typically the type of Johnson-Cook (J-C) (flow stress) equations, is mathematically verified and a criterion of J-C type equation is provided. The mechanical experimental data are assembled as 2D matrix (two variables of strain and strain-rate or temperature) or 3D array (three variables of strain, strain-rate and temperature). With low-rank approximation method, i.e. singular value decomposition (SVD) for 2D matrix and CANDECOMP/PARAFAC (CP) for 3D array, the experimental data matrix/array (N) can be decomposed into a heavily-weighted matrix/array (N1) and several matrices/arrays with reduced weights (N2, N3, …). The criterion of J-C type equation is that N1 can approximately represent N with acceptable error. Otherwise, the use of J-C type equation is invalid. A method to describe the dynamic flow stress based on discrete experimental dataset is further proposed based on the SVD/CP method. The advantages of coupled and decoupled flow stress equations and the problems associated with the original J-C equation are discussed.

Learning-Based Shared Control Using Gaussian Processes for Obstacle Avoidance in Teleoperated Robots

Physically inspired models of the stochastic nature of the human-robot-environment interaction are generally difficult to derive from first principles, thus alternative data-driven approaches are an attractive option. In this article, Gaussian process regression is used to model a safe stop maneuver for a teleoperated robot. In the proposed approach, a limited number of discrete experimental training data points are acquired to fit (or learn) a Gaussian process model, which is then used to predict the evolution of the process over a desired continuous range (or domain). A confidence measure for those predictions is used as a tuning parameter in a shared control algorithm, and it is demonstrated that it can be used to assist a human operator by providing (low-level) obstacle avoidance when they utilize the robot to carry out safety-critical tasks that involve remote navigation using the robot. The algorithm is personalized in the sense that it can be tuned to match the specific driving style of the person that is teleoperating the robot over a specific terrain. Experimental results demonstrate that with the proposed shared controller enabled, the human operator is able to more easily maneuver the robot in environments with (potentially dangerous) static obstacles, thus keeping the robot safe and preserving the original state of the surroundings. The future evolution of this work will be to apply this shared controller to mobile robots that are being deployed to inspect hazardous nuclear environments, ensuring that they operate with increased safety.

Advanced Model of Spatiotemporal Mining-Induced Kinematic Excitation for Multiple-Support Bridges Based on the Regional Seismicity Characteristics

In the paper, an advanced model of spatiotemporal mining-induced kinematic excitation (SMIKE) for multiple-support bridges exposed to mining-induced seismicity is proposed. The uniqueness of this model results from the possibility of its application in any region of mining activity, as it is based on empirical regression functions characterizing such regions. In the model, the loss of coherency resulting from the scattering of waves in the heterogeneous ground, the wave-passage effect originating in different arrival times of waves to consecutive supports, and the site-response effect depending on the local soil conditions are taken into account. The loss of coherency of mining-induced seismic waves is obtained by applying a random field generator based on a spatial correlation function to produce time histories of accelerations on consecutive structure supports based on an originally recorded shock. The deterministic approach is used to account for temporal wave variability. The proposed SMIKE model is applied to assess the dynamic performance of a five-span bridge under a mining-induced shock recorded in the Upper Silesian Coal Basin (USCB), Poland. The first model’s parameter (space scale parameter) is identified on the basis of regression curves defined for the USCB region. The estimation of the second parameter (the mean apparent wave passage velocity) is based on discrete experimental data acquired via the vibroseis excitation registered in the in situ experiment. The impact of the model application on the dynamic performance of the bridge is assessed by comparing the dynamic response levels under SMIKE excitations, classic uniform excitations, and the “traveling wave” model—accounting only for the wave passage effect. The influence of wave velocity occurs to be crucial, modifying (either amplifying or reducing, depending on the location of the analyzed point) the dynamic response level up to a factor of two. The introduction of the space scale parameter changes the results by 20% in relation to the outcomes obtained for the “traveling” wave only.

Prediction of saturated hydraulic conductivity of sandy soil using Sauter mean diameter of soil particles

AbstractSaturated hydraulic conductivity of soil, Ksat, is a critical parameter for mathematical modelling of groundwater and soil water flow. Current empirical Ksat models, such as Kozeny–Carman model, Hazen models, or empirical approaches based on soil pedotranfer functions (PTFs) rely on the specific surface area, certain characteristic particle size(s), for example, d5, d10, d15, d50, etc., or the fractionation of sand, silt and clay. In this study, we proposed an alternative modelling framework to estimate Ksat from void ratio and arithmetic mean diameter of soil particles. We suggested to describe the particle size distribution (PSD) using a continuous mathematical distribution function, for example, the lognormal distribution function, and to obtain the Sauter mean diameter (dS) by calculating the moments of the PSD function. The proposed empirical equations were tested against the measured Ksat values for 54 sand samples with available measured PSD and void ratio as reported in Toumpanou et al. and validated using 49 sandy soil samples obtained from the Unsaturated Soil Database (UNSODA). We found that the dS was more relevant to Ksat than the number mean diameter (dN) and mass mean diameter (dM) (the RMSE was 0.486 for Ksat ‐ dN; 0.187 for Ksat ‐ dS; and 0.212 for Ksat ‐ dM, respectively). We also found that the proposed equation with dS had superior performance over the Hazen equation and PTF‐based models. Our results indicated that the Ksat calculated using dS, which was either estimated from discrete experimental data or calculated from a continuous lognormal PSD function, best matched with the measured values for coarse‐textured soils. Future studies should focus on developing empirical relationships for soil water retention curves and other soil properties based on continuous PSD functions.Highlights A modelling framework to estimate Ksat from the void ratio and arithmetic mean diameter is proposed. Sauter mean diameter, dS, can be obtained by calculating the moments of the PSD function. Ksat model using dS performs better than that using dN, dM, d5, d10, or d50 or PTF‐based models.

Sample capacity and anvil size effects for a standardized method to determine the delamination strength of 2G HTS coated conductors

Anvil tests are effective for determination of the mechanical delamination strength (MDS) of 2G high temperature superconducting coated conductors (CCs), which has been widely used. However, the discrete property of measurement data makes the MDS of CCs hard to evaluate just from the average and variance. To properly provide an analysis of these discrete experimental data, the three-parameter Weibull distribution analysis along with its reliability function has been adopted, from the criterion of 99% reliability, the obtained MDS is conveniently referred for both engineering test and design. Nevertheless, the stable and reliable Weibull distribution analysis should be based on enough sample capacity and proper anvil size. In this work, the influences of anvil size and sample capacity on the reliability function of Weibull distribution analysis for MDS determination were systematically investigated at 77K. It is found that the minimum of sample capacities for Weibull distribution analysis are different with different anvil sizes. From the aspects of energy dissipation and cost consumption, we recommend the full width size of anvils to be utilized to obtain the justified MDS of CCs without being slitted.

Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin

Prediction of a precise subsoiling using an analytical model (AM) and Discrete Element Method (DEM) was conducted to explain cutting forces and the soil profile induced changes by a subsoiler. Although sensors, AMs and DEM exist, there are still cases of soil structure deformation during deep tillage. Therefore, this study aimed to provide a clear understanding of the deep tillage using prediction models. Experimental data obtained in the soil bin trolley with force sensors were used for verification of the models. Experiments were designed using Taguchi method. In the AM, the modified-McKyes and Willat and Willis equations were used to determine cutting forces and soil furrow profile respectively. Calculations were done using MATLAB software. The elastoplastic behavior of soil was incorporated into the DEM. The DEM predicted results with the best regression of 0.984 R^{2} at a NRMSE of 1.936 while the AM had the lowest R^{2} of 0.957, at a NRMSE of 6.008. All regression results were obtained at p < 0.05. The ANOVA test showed that the p-values for the horizontal and vertical forces were 0.9396 and 0.9696, respectively. The DEM predicted better than the AM. DEM is easy to use and is effective in developing models for precision subsoiling.

An innovative method for the characterization of oil content in lacustrine shale-oil systems: A case study from the Middle Permian Lucaogou Formation in the Jimusaer Sag, Junggar Basin

Determination of oil content not only affects the exploration and development of potential favorable intervals in lacustrine shale-oil systems but also impacts research on the main factors controlling the differential accumulation of shale oil. Current approaches usually characterize a certain fraction of the hydrocarbons in shale oil. For example, the volatile hydrocarbon (S1) fraction is considered to represent the hydrocarbons (C7–C33) that would be volatilized below 300 °C. In this article, an innovative method called the oil content evaluation index (OCEI) is proposed, in which the shale oil composition is considered and used to evaluate the vertical distribution of oil content utilizing conventional logging. In the OCEI method, the evaluation index (LI*) represents the content of liquid hydrocarbons per unit mass of rock; this index successfully characterizes the discrete experimental data S1 and chloroform extract yield. The evaluation index (CI*) represents the oil-bearing area under the core description. The evaluation index (GI*) characterizes the content of gaseous hydrocarbons in a constant volume. Based on the relationship between these evaluation indexes and the actual production, the relative weight of each evaluation index is determined through the grey relational analysis method. Finally, the OCEI value is the sum of the product of these normalized evaluation indicators and their corresponding weights. In a case study, the method is successfully implemented to predict the pay zones of the Lucaogou Formation in the Jimsar Sag, Junggar Basin, China. The threshold value of OCEI is set to 0.39 based on its relationship with production data. When the OCEI value of a certain interval is greater than 0.39, then this interval is a favorable pay zone. The evaluation results of two wells in the Jimusaer Sag further demonstrate that the model can effectively and reliably predict potential favorable intervals in shale oil systems.

Strain depth profiles in thin films extracted from in-plane X-ray diffraction

Thin films generally contain depth-dependent residual stress gradients, which influence their functional properties and stability in harsh environments. An understanding of these stress gradients and their influence is crucial for many applications. Standard methods for thin-film stress determination only provide average strain values, thus disregarding possible variation in strain/stress across the film thickness. This work introduces a new method to derive depth-dependent strain profiles in thin films with thicknesses in the submicrometre range by laboratory-based in-plane grazing X-ray diffraction, as applied to magnetron-sputtering-grown polycrystalline Cu thin films with different thicknesses. By performing in-plane grazing diffraction analysis at different incidence angles, the in-plane lattice constant depth profile of the thin film can be resolved through a dedicated robust data processing procedure. Owing to the underlying intrinsic difficulties related to the inverse Laplace transform of discrete experimental data sets, four complementary procedures are presented to reliably extract the strain depth profile of the films from the diffraction data. Surprisingly, the strain depth profile is not monotonic and possesses a complex shape: highly compressive close to the substrate interface, more tensile within the film and relaxed close to the film surface. The same strain profile is obtained by the four different data evaluation methods, confirming the validity of the derived depth-dependent strain profiles as a function of the film thickness. Comparison of the obtained results with the average in-plane stresses independently derived by the standard stress analysis method in the out-of-plane diffraction geometry validates the solidity of the proposed method.

Connecting Chemistry to Mathematics by Establishing the Relationship between Conductivity and Concentration in an Interdisciplinary, Computer-Based Project for High School Chemistry Students

Here, we present an interdisciplinary educational project intended for high school chemistry that is focused on the establishment of a mathematical model to describe the relationship between conductivity and concentration, while linking chemistry to mathematics with the help of computer software. The project was designed to help high school students engage in methods and comprehend advanced topics beyond the typical high school chemistry curriculum, as well as to resolve a problem that no single disciplinary perspective is able to solve. This project provided students with a simple method to build mathematical models, which could be used to help them discover the relationship between discrete experimental data and mathematical equations. A postinstructional survey showed that, after completing the project, more students knew how to construct mathematical equations that fit discrete data.

Degradation modeling of poly-l-lactide acid (PLLA) bioresorbable vascular scaffold within a coronary artery.

In this work, a strain-based degradation model was implemented and validated to better understand the dynamic interactions between the bioresorbable vascular scaffold (BVS) and the artery during the degradation process. Integrating the strain-modulated degradation equation into commercial finite element codes allows a better control and visualization of local mechanical parameters. Both strut thinning and discontinuity of the stent struts within an artery were captured and visualized. The predicted results in terms of mass loss and fracture locations were validated by the documented experimental observations. In addition, results suggested that the heterogeneous degradation of the stent depends on its strain distribution following deployment. Degradation is faster at the locations with higher strains and resulted in the strut thinning and discontinuity, which contributes to the continuous mass loss, and the reduced contact force between the BVS and artery. A nonlinear relationship between the maximum principal strain of the stent and the fracture time was obtained, which could be transformed to predict the degradation process of the BVS in different mechanical environments. The developed computational model provided more insights into the degradation process, which could complement the discrete experimental data for improving the design and clinical management of the BVS.

Optimization of grading guard systems for trapping of particulates to prevent pressure drop buildup in gas oil hydrotreater

A new approach to optimize grading guard systems for gas oil hydrotreating reactor based on the phenomenon of clogging of catalyst beds with microparticulates and coke deposits was developed. Pellets with different shapes have been tested to obtain dynamic dependencies of filter coefficient and pressure drop buildup. The experiments were performed in the trickle-flow regime with different ensembles of microparticulates having sizes from 15 to 165 μm. The observed effect of a filter cake formation was characterized in terms of average porosity of the interface beds and it was introduced in simulations plugging of catalyst bed. Estimation of the growth of coke deposits was made using modified Voorhies’s equation and experimental samples obtained in the microreactor under hydrotreating process conditions using foulants of industrial gas oil hydrotreater. The analysis of coked samples was carried out using TGA. Based on the discrete model and experimental data, optimization procedure of grading guard systems was carried out using brute force search. All combinations of schemes of guard-beds were compared using integrated hydrodynamic resistance as a criterion. It was shown that an optimized scheme of guard-beds can reduce hydrodynamic resistance as compared to the schemes without guard-beds or with non-optimized guard-beds.

Direct Reconstruction of the Experimental Data in the Case of Ill-Conditioned Problems and in the Presence of Data Distortions

The possibilities of reconstructing discrete experimental data using direct inversion, i.e., deconvolution, are investigated. Methods for optimization, smoothing, compensation, and decomposition are proposed, with which it is possible to reconstruct data with minimal information losses in many cases of ill-conditioned problems and in the presence of data distortions. These methods are based on the formation of well-conditioned systems with the compensation of accidental distortions from overdetermined systems of equations corresponding to the convolution equations using the linear transformations. A comparative analysis of the proposed methods is carried out. Their potentialities and accuracies of reconstruction are analyzed. Examples of the reconstruction are presented.

ON SOLVING BIOLOGICAL PROBLEM BASED ON FUNCTIONAL-DIFFERENTIAL EQUATIONS OF DELAY TYPE WITH DISCRETE EXPERIMENTAL DATA.

ON SOLVING BIOLOGICAL PROBLEM BASED ON FUNCTIONAL-DIFFERENTIAL EQUATIONS OF DELAY TYPE WITH DISCRETE EXPERIMENTAL DATA.

Comparisons of single-phase and two-phase models for numerical predictions of Al2O3/water nanofluids convective heat transfer

This paper numerically studies Al2O3/water nanofluids that convectively flow inside the laminar regime of a tube with a constant wall heat flux with various models. Eight different models are utilized to predict the heat transfer behavior of the nanofluids, and the predictions are compared with the available experimental data from literature and the values of the conventional correlation. Comparisons show that all eight models are suitable for the prediction of Al2O3 nanofluids with relatively small particle concentrations (0.25 wt% and 0.5 wt%, mass fractions) of Al2O3/water, within the maximum deviation between the predictions of all models and corresponding experimental data less than 20%. While comparison results with experiments of relatively large particle concentrations (0.6%, 1.0% and 1.6%, volume fractions) show that Mixture model overestimates the heat transfer performance. Discrete phase model increases the prediction accuracy about 10% for two-phase models and agrees well with the classical Shah Equation within the maximum error of 5.5%. The error of Nusselt number between the predictions of discrete phase model and experimental data falls off with the increase of Reynolds numbers and axial direction position. The discrete phase model, Xuan-Roetzel dispersion model, and Talieh-Abbas dispersion model are precise approaches to predict the laminar convectional heat transfer behavior of Al2O3/water nanofluids in the scope of 0–1.6% nanoparticles. The Xuan-Roetzel dispersion model and Talieh-Abbas dispersion model are suggested for applications where the calibration data are available.

Biomass steam gasification in bubbling fluidized bed for higher-H2 syngas: CFD simulation with coarse grain model

A comprehensive coarse grain model (CGM) is applied to simulation of biomass steam gasification in bubbling fluidized bed reactor. The CGM was evaluated by comparing the hydrodynamic behavior and heat transfer prediction with the results predicted using the discrete element method (DEM) and experimental data in a lab-scale fluidized bed furnace. CGM shows good performance and the computational time is significantly shorter than the DEM approach. The CGM is used to study the effects of different operating temperature and steam/biomass (S/B) ratio on the gasification process and product gas composition. The results show that higher temperature enhances the production of CO, and higher S/B ratio improves the production of H2, while it suppresses the production of CO. For the main product H2, the minimum relative error of CGM in comparison with experiment is 1%, the maximum relative error is less than 4%. For the total gas yield and H2 gas yield, the maximum relative errors are less than 7%. The predicted concentration of different product gases is in good agreement with experimental data. CGM is shown to provide reliable prediction of the gasification process in fluidized bed furnace with considerably reduced computational time.